How to Automate Machine Learning with GitHub Actions
Automate machine learning workflows using GitHub Actions to speed up development cycles and streamline model deployment.
MLOps That Works: Building ML Pipelines for Autonomous Factory Models
Explore MLOps strategies for autonomous factories. Learn design patterns and anti-patterns for deploying ML models in dynamic industrial environments using flexible pipelines.
DevOps for ML and Other Half-Truths - Processes and Tools for the ML Lifecycle
Explore processes and tools for enterprise-ready ML deployment, addressing the challenges of standardization in the ML lifecycle and supporting IT operations management.
GitOps, CI/CD, and MLOps - How It All Comes Together for Developers
Explore GitOps, CI/CD, and MLOps integration in OpenShift development workflows. Learn to leverage Jupyter Notebooks, OpenShift Pipelines, and ArgoCD for efficient intelligent application management.
The Fun Sized MLOps Stack from Scratch
Build a fully-fledged MLOps platform for small companies using open-source tools. Learn about common problems, popular tools, recent OSS projects, and a realistic roadmap for improving data and ML maturity.
Building an ML Experimentation Platform for Easy Reproducibility
Learn to build an ML experimentation platform using lakeFS for version control and reproducibility. Explore creating frameworks, reproducing iterations, and building intuitive infrastructure with open-source tools.
Testing Models and Data in CI/CD Pipelines for Machine Learning
Explore best practices for testing ML models in CI/CD pipelines. Learn to validate models and data using deepchecks, addressing common pitfalls and ensuring continuous relevance and trustworthiness.
Developing Kubeflow Pipeline Components for Vertex AI
Develop and deploy Kubeflow pipeline components for Vertex AI, exploring features, use cases, and visualization techniques for efficient ML workflows.
AutoML in Vertex AI for MLOps
Explore AutoML in Vertex AI for MLOps, streamlining model training, comparison, and deployment on Google Cloud's unified platform.
Amazon SageMaker for MLOps
Explore automated machine learning with Amazon SageMaker, covering AutoML for model training and deployment to AWS endpoints.
MLOps on Google Vertex AI: Enhancing Machine Learning Workflows
Explore Google's Vertex AI suite to enhance MLOps efficiency and management in machine learning workflows, focusing on its comprehensive capabilities.
MLOps on Azure Machine Learning Studio
Automate and streamline machine learning lifecycle with MLOps on Azure ML, integrating ML, DevOps, and Data Engineering for consistent, high-quality model delivery.
Building Your ML Ops Strategy for Generative AI
Adapt ML Ops for generative AI with tuning pipelines, model management, evaluation, and safety filters. Learn to operationalize and customize models effectively.
Goodbye, Deployment Headaches - Cloud Deploy and Vertex AI Integration
Automate ML model deployment with Cloud Deploy and Vertex AI integration. Test, validate, and deploy in minutes without coding, simplifying the MLOps lifecycle.
Operationalizing Generative AI on Vertex AI
Explore MLOps fundamentals, adaptations for generative AI, and Vertex AI capabilities to support new workflows in the era of foundation models.
Reinvent Yourself and Be Curious - MLOps Career Insights - Podcast #264
Explore MLOps career transitions, essential pipeline components, and practical challenges with insights from an experienced engineer. Gain valuable perspectives on industry trends and best practices.
Obtaining New Insights on Model Behavior with Fiddler - MLOps Meetup #110
Gain insights on model behavior using Fiddler for monitoring, explainability, and analysis. Learn to instrument ML models for observability, register models, and publish events to understand data drift and underperforming cohorts.
Managing Machine Learning Projects - From Design to Deployment
Explore ML project management strategies, from setup to production integration. Learn to navigate challenges, assess models, and align with business processes for successful implementation.
MLOps for Ad Platforms - Lessons from Ad Tech - Coffee Session 130
Explore MLOps in ad platforms with Andrew Yates. Learn about designing ML components, monitoring systems, and optimizing billion-dollar content marketplaces for long-term revenue and user experience.
Unleashing Sensitive Datasets with Distributed Data Science
Explore distributed data science techniques for extracting value from sensitive datasets while maintaining privacy and security. Learn about federated machine learning, secure aggregation, and practical applications in various configurations.
Real-time Machine Learning - MLOps Coffee Sessions
Explore real-time machine learning with Chip Huyen, covering learning strategies, MLOps challenges, and streaming complexities. Gain insights on when to implement real-time solutions and avoid common company mistakes.